library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(tsibbledata)
## Warning: package 'tsibbledata' was built under R version 4.1.3
library(tsibble)
## Warning: package 'tsibble' was built under R version 4.1.3
## 
## Attaching package: 'tsibble'
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, union
library(lubridate)
## Warning: package 'lubridate' was built under R version 4.1.3
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:tsibble':
## 
##     interval
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(ggplot2)
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 4.1.3
nyc_bikes_df <- tsibbledata::nyc_bikes
nyc_bikes_df
# glimpse(nyc_bikes_df)
# view(nyc_bikes_df)
  1. Adding columns: start day of the week, month, time
  2. Creating a column that shows the season
  3. Creating a column for age of rider and removing two outliers (born 1880s)
  4. Showing hire durations
#creating 4 columns: date, and start day, month and hour
nyc_bikes_df <- nyc_bikes_df %>% 
  mutate(date = as_date(start_time), .after = 2,
         start_day = wday(start_time, label = TRUE, abbr = FALSE),
         start_month = month(start_time, label = TRUE, abbr = FALSE),
         start_hour = hour(start_time), minute(start_time))
#Couldn't unite the start time hours and mins but I am only going to look at 
#hour anyway so it doesn't matter:
nyc_bikes_df <- nyc_bikes_df %>% 
  unite(start_hire_time, 
        start_hour, `minute(start_time)`, 
        sep = ":", 
        remove = FALSE)
nyc_bikes_df
#adding season column just in case
nyc_bikes_df <- nyc_bikes_df %>% 
  mutate(season = case_when(
    start_month %in% c("December", "January", "February") ~ "Winter",
    start_month %in% c("March", "April", "May") ~ "Spring",
    start_month %in% c("June", "July", "August") ~ "Summer",
    start_month %in% c("September", "October", "November") ~ "Autumn",
    ), .after = 5)
nyc_bikes_df
# showing duration of hire (2 were hired for 3 days and the rest less than a day
# most were hired for less than 50 mins)
nyc_bikes_df <- nyc_bikes_df %>% 
  mutate(hire_duration = stop_time - start_time, # this shows 1.50(= 1min 30sec)
         length_of_hire_rounded = as.period(round(stop_time - start_time)), 
         hours_hired = as.numeric(length_of_hire_rounded, "hours")
  )
nyc_bikes_df
#Finding people's ages in 2018 - 2018 was the year the data was collected
nyc_bikes_df <- nyc_bikes_df %>% 
  mutate(age_years = as.numeric(2018 - birth_year))  
nyc_bikes_df
# There are two rows where the customer is born in the 1880s. That can not be
# right so I am removing those two rows (neither has a gender either)
nyc_bikes_df <- nyc_bikes_df %>% 
  filter(birth_year > 1900)
nyc_bikes_df

Looking at subscribers, customers and unknown genders

# 3951 are subscribers 
nyc_bikes_df %>% 
  filter(type == "Subscriber")
# 315 are customers
nyc_bikes_df %>% 
  filter(type == "Customer")
# 267 have gender = "unknown" and I noticed that most are 49 years old (n = 243)
nyc_bikes_df %>% 
  filter(gender == "Unknown" & age_years == 49)
# 49 year olds:
# 243/269 have gender unknown (26 have a gender).
# 89/269 are subscribers, the rest (180) are customers (out of 315 in total)
age49 <- nyc_bikes_df %>% 
  filter(age_years == 49)
# view(age49)

What is the pattern of bike hires over time (e.g. within a year, month,

week, or day)?

# view(nyc_bikes_df)
# 4,266 rows

Across the year, total bikes hired = 4,266 rows

Total rentals per day:

count_per_day <- nyc_bikes_df %>% 
  group_by(date) %>% 
  summarise(rentals_per_day = n()) %>% 
  arrange(desc(rentals_per_day))
count_per_day
count_per_day %>% 
  ggplot()+
  aes(x = date, y = rentals_per_day)+
  geom_line()

From the graph, we can see an increase in total rentals per day as we head from winter into summer (the spike is on 31st July, a Tuesday) and then a decrease again as temperatures cool as we head into autumn and winter again.

Though a trend can be seen, it is a bit messy. To look at it another way we can look at the total bikes rented per month or by season:

count_per_month <- nyc_bikes_df %>% 
  group_by(season, start_month) %>% 
  summarise(rentals_per_day = n()) %>% 
  arrange(desc(rentals_per_day))
## `summarise()` has grouped output by 'season'. You can override using the
## `.groups` argument.
count_per_month
count_per_month %>% 
  ggplot()+
  aes(x = start_month, y = rentals_per_day)+
  geom_col()+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))+
  geom_text(aes(label = rentals_per_day),
    position = position_dodge(0.9),
    vjust = 0)

count_per_month %>% 
  ggplot()+
  aes(x = season, y = rentals_per_day)+
  geom_col()

Total bikes hired per day:

weekdays <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")

#Tues: 720!, Fri 658, Wed, Thurs, Mon 628, Sat 523, Sun(453!))
bikes_per_day <- nyc_bikes_df %>% 
  group_by(start_day) %>% 
  summarise(num_of_bikes_hired = n()) %>% 
  arrange(desc(num_of_bikes_hired))
bikes_per_day
bikes_per_day %>%  
  ggplot(aes(x = start_day, y = num_of_bikes_hired))+
  scale_x_discrete(limits = weekdays) +
    geom_col()+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))+
  geom_text(aes(label = num_of_bikes_hired),
    position = position_dodge(0.9),
    vjust = 0)

From above, we can see that weekdays are more popular bike hire days than weekends. 720 bikes were hired on Tuesdays compared with 453 on Sundays. Was this the case every month?

monthly_bikes_per_day <-nyc_bikes_df %>% 
  group_by(season, start_month, start_day) %>% 
  summarise(num_of_bikes_hired = n())
## `summarise()` has grouped output by 'season', 'start_month'. You can override
## using the `.groups` argument.
monthly_bikes_per_day
monthly_bikes_per_day %>% 
  ggplot()+
  aes(x = start_day, y = num_of_bikes_hired, 
      fill = start_day)+
  scale_x_discrete(limits = weekdays) +
  geom_col() + 
  facet_wrap(~start_month)+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

monthly_bikes_per_day %>% 
  ggplot()+
  aes(x = start_day, y = num_of_bikes_hired, 
      group = start_month, colour = start_month)+
  scale_x_discrete(limits = weekdays) +
  geom_line()+
  facet_wrap(~season)+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

Are people mostly travelling at commuting times then? Looking at the full year:

time_of_ride <- nyc_bikes_df %>% 
  group_by(start_hour) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
time_of_ride
time_of_ride %>% 
  ggplot()+
  aes(x = start_hour, y = count)+
  geom_col()+
  geom_text(aes(label = count),
    position = position_dodge(0.9),
    vjust = 0)

Looking by day and month:

#month
time_of_ride_by_month <- nyc_bikes_df %>% 
  group_by(start_month, start_hour) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
## `summarise()` has grouped output by 'start_month'. You can override using the
## `.groups` argument.
time_of_ride
time_of_ride_by_month %>% 
  ggplot()+
  aes(x = start_hour, y = count)+
  geom_col()+
  facet_wrap(~start_month)

#day
time_of_ride_by_day <- nyc_bikes_df %>% 
  group_by(start_day, start_hour) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
## `summarise()` has grouped output by 'start_day'. You can override using the
## `.groups` argument.
time_of_ride
time_of_ride_by_day %>% 
  ggplot()+
  aes(x = start_hour, y = count)+
  geom_col()+
  facet_wrap(~start_day)

Demographics

#there are 3069 males, 930 females, 267 unknown (243 of them are "49" years old)
nyc_bikes_df %>% 
  group_by(gender) %>% 
  summarise(count = n())
# 3,999 rows when taking out gender unknown
minus_gender_unknown <- nyc_bikes_df %>% 
  filter(gender %in% c("Male", "Female"))
minus_gender_unknown

How many trips per gender?

rentals_by_gender <- minus_gender_unknown %>% 
  group_by(date, gender) %>% 
  summarise(rentals_per_day = n()) 
## `summarise()` has grouped output by 'date'. You can override using the
## `.groups` argument.
rentals_by_gender
rentals_by_gender %>% 
  ggplot()+
  aes(x = date, y = rentals_per_day)+
  geom_point() +
  facet_grid(~gender)+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

rentals_by_gender %>% 
  filter(gender == "Male") %>% 
  ggplot()+
  aes(x = date, y = rentals_per_day)+
  geom_point()

  rentals_by_gender %>% 
  filter(gender == "Female") %>% 
  ggplot()+
  aes(x = date, y = rentals_per_day)+
  geom_point()+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

Do males and females hire at different times of year/ days of week ?

#Tues: 720!, Fri 658, Wed, Thurs, Mon 628, Sat 523, Sun(453!))
day_by_gender <- minus_gender_unknown %>% 
  group_by(start_day, gender) %>% 
  summarise(daily_users = n())
## `summarise()` has grouped output by 'start_day'. You can override using the
## `.groups` argument.
day_by_gender
day_by_gender %>% 
  ggplot(aes(x = start_day, y = daily_users, fill = gender))+
  scale_x_discrete(limits = weekdays) +
  geom_col(position = "dodge")+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))+
  geom_text(aes(label = daily_users),
    position = position_dodge(0.9),
    vjust = 0)

month_by_gender <- minus_gender_unknown %>% 
  group_by(start_month, start_day, gender) %>% 
  summarise(daily_users = n())
## `summarise()` has grouped output by 'start_month', 'start_day'. You can
## override using the `.groups` argument.
month_by_gender
month_by_gender %>% 
  ggplot(aes(x = start_day, y = daily_users, fill = gender))+
  scale_x_discrete(limits = weekdays) +
  geom_col(position = "dodge") +
  facet_wrap(~start_month)+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

Difference in gender start location? (stereotype for business vs holiday)

# same top 2 anyway 
minus_gender_unknown %>% 
  group_by(start_station, gender) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
## `summarise()` has grouped output by 'start_station'. You can override using the
## `.groups` argument.

AGE

How many rides per age

nyc_bikes_df %>% 
  group_by(age_years) %>% 
  ggplot()+
  aes(x = age_years) +
  geom_bar()

rides per age by gender

minus_gender_unknown %>% 
  group_by(age_years, gender) %>% 
  ggplot()+
  aes(x = age_years, fill = gender) +
  geom_bar()

Type of trip

subscriber or not:

# nyc_bikes_df %>% 
#   filter(type == "Subscriber")
# 
# nyc_bikes_df %>% 
#   filter(type == "Customer")
# 
# nyc_bikes_df %>% 
#   filter(is.na(type))
# 3953 rows are subscribers (true) 
# and 315 are customers (FALSE)
# 0 NAs.
# with the 49 year olds: 89/269 are subscribers, the rest (180) are customers (out of 315 in total)

DURATION:

duration <- nyc_bikes_df %>% 
  group_by(length_of_hire_rounded) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count)) 

a <- nyc_bikes_df %>% 
  mutate(duration_block = case_when(
    length_of_hire_rounded <= "5M 0S" ~ "0 - 5", 
    length_of_hire_rounded <= "10M 0S" & 
      length_of_hire_rounded > "5M 0S" ~ "5 - 10",
    length_of_hire_rounded <= "15M 0S" & 
      length_of_hire_rounded > "10M 0S" ~ "10 - 15",
    length_of_hire_rounded <= "20M 0S" & 
      length_of_hire_rounded > "15M 0S" ~ "15 - 20",
    length_of_hire_rounded <= "25M 0S" & 
      length_of_hire_rounded > "20M 0S" ~ "20 - 25",
    length_of_hire_rounded <= "30M 0S" & 
      length_of_hire_rounded > "25M 0S" ~ "25 - 30",
    length_of_hire_rounded <= "35M 0S" & 
      length_of_hire_rounded > "30M 0S" ~ "30 - 35",
    length_of_hire_rounded <= "40M 0S" & 
      length_of_hire_rounded > "35M 0S" ~ "35 - 40",
    length_of_hire_rounded <= "45M 0S" & 
      length_of_hire_rounded > "40M 0S" ~ "40 - 45",
    length_of_hire_rounded <= "50M 0S" & 
      length_of_hire_rounded > "45M 0S" ~ "45 - 50",
    length_of_hire_rounded <= "55M 0S" & 
      length_of_hire_rounded > "50M 0S" ~ "50 - 55",
    length_of_hire_rounded <= "60M 0S" & 
      length_of_hire_rounded > "55M 0S" ~ "55 - 60",
    length_of_hire_rounded <= "3H 0S" & 
      length_of_hire_rounded > "1H 0S" ~ "1Hrs - 3Hrs",
    length_of_hire_rounded > "3H 0S" ~ "3Hrs plus",
  ))


durations <- a %>% 
  group_by(duration_block) %>% 
  summarise(count = n()) 
durations
time_blocks <- c("0 - 5", "5 - 10", "10 - 15", "15 - 20", "20 - 25", "25 - 30",
                 "30 - 35", "35 - 40", "40 - 45", "45 - 50", "50 - 55", 
                 "55 - 60", "1Hrs - 3Hrs", "3Hrs plus")

durations %>%   
  ggplot()+
  aes(x = duration_block, y = count) +
  scale_x_discrete(limits = time_blocks) +
  geom_col()+
  theme(axis.text.x = element_text(angle=45, hjust=0.9))

GEOGRAPHY

Duration vs starting location There are 52 start stations and 55 end stations

start_coords <- nyc_bikes_df %>% 
  unite("start_coords", start_lat, start_long, sep = ", ", remove = FALSE)
start_coords
library(sf) # "simple features"
## Warning: package 'sf' was built under R version 4.1.3
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(rgeos)
## Warning: package 'rgeos' was built under R version 4.1.3
## Loading required package: sp
## Warning: package 'sp' was built under R version 4.1.3
## rgeos version: 0.5-9, (SVN revision 684)
##  GEOS runtime version: 3.9.1-CAPI-1.14.2 
##  Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
##  GEOS using OverlayNG
##  Linking to sp version: 1.4-6 
##  Polygon checking: TRUE
library(rnaturalearth)
## Warning: package 'rnaturalearth' was built under R version 4.1.3
library(rnaturalearthdata)
## Warning: package 'rnaturalearthdata' was built under R version 4.1.3
library(leaflet)
## Warning: package 'leaflet' was built under R version 4.1.3

How many bikes from each station (start)

# how many bikes from each station (52 start stations): 
bikes_per_start_station <- nyc_bikes_df %>% 
  group_by(start_long, start_lat, start_station, type) %>% 
  summarise(total_hires = n(), average_duration = round(mean(hire_duration))) %>% 
  arrange(desc(total_hires))
## `summarise()` has grouped output by 'start_long', 'start_lat', 'start_station'.
## You can override using the `.groups` argument.
bikes_per_start_station
# 55 end stations
bikes_per_end_station<- nyc_bikes_df %>%
  group_by(end_long, end_lat, end_station, type) %>% 
  summarise(total_returns = n()) %>% 
  arrange(desc(total_returns))
## `summarise()` has grouped output by 'end_long', 'end_lat', 'end_station'. You
## can override using the `.groups` argument.
bikes_per_end_station
#plot on scatter??
bikes_per_start_station %>% 
  ggplot()+
  aes(x = start_station, y = total_hires, shape = type)+
  geom_point()

#there are too many, filter for most popular
bikes_per_start_station %>% 
  filter(total_hires > 90) %>% 
  ggplot()+
  aes(x = start_station, y = total_hires, fill = type)+
  geom_col()

looking at radius:

bikes_per_start_station %>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~start_long, 
                   lat = ~start_lat,
                   radius = ~total_hires/10,
                   popup = ~paste0("Start Station: ", start_station, 
                                   "<br> Total Hires: ", total_hires, 
                                   "<br> Average Duration: ", average_duration, 
                                   " mins"))

radius in clusters (though I think it’s better to see out in the open)

bikes_per_start_station %>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~start_long, 
                   lat = ~start_lat,
                   radius = ~total_hires/2,
                   clusterOptions = markerClusterOptions(),
                   popup = ~paste0("Start Station: ", start_station, 
                                   "<br> Total Hires: ", total_hires))
bikes_per_end_station%>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~end_long, 
                   lat = ~end_lat,
                   radius = ~total_returns/10,
                   popup = ~paste0("End Station: ", end_station,
                                   "<br>Total Returns ", total_returns))

DURATION BY STATION

nyc_bikes_df %>% 
  group_by(start_station, type) %>%
  summarise(count = n())
## `summarise()` has grouped output by 'start_station'. You can override using the
## `.groups` argument.